Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Unsupervised Feature Selection via Robust Autoencoder and Adaptive Graph Learning
Effective feature selection is essential for high-dimensional data analysis and machine learning. Unsupervised feature selection (UFS) aims to simultaneously cluster data and identify the most discriminative features. Mo…
Graph LearningAutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation
Hierarchical representations provide powerful and principled approaches for analyzing many musical genres. Such representations have been broadly studied in music theory, for instance via Schenkerian analysis (SchA). Hie…
Graph LearningAL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge. Existing methods particularly those base…
Graph ClassificationGraph LearningSharpness-aware Federated Graph Learning
One of many impediments to applying graph neural networks (GNNs) to large-scale real-world graph data is the challenge of centralized training, which requires aggregating data from different organizations, raising privac…
Graph ClassificationGraph LearningParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the over-smoothing problem in deep GNNs, global…
Graph Representation LearningGraph ClassificationNode ClassificationGraph LearningTowards Practical Large-scale Dynamical Heterogeneous Graph Embedding: Cold-start Resilient Recommendation
Deploying dynamic heterogeneous graph embeddings in production faces key challenges of scalability, data freshness, and cold-start. This paper introduces a practical, two-stage solution that balances deep graph represent…
Graph EmbeddingGraph LearningTopologically-Stabilized Graph Neural Networks: Empirical Robustness Across Domains
Graph Neural Networks (GNNs) have become the standard for graph representation learning but remain vulnerable to structural perturbations. We propose a novel framework that integrates persistent homology features with st…
Graph Representation LearningGraph LearningCo-Hub Node Based Multiview Graph Learning with Theoretical Guarantees
Identifying the graphical structure underlying the observed multivariate data is essential in numerous applications. Current methodologies are predominantly confined to deducing a singular graph under the presumption tha…
Graph LearningHarnessing Rich Multi-Modal Data for Spatial-Temporal Homophily-Embedded Graph Learning Across Domains and Localities
Modern cities are increasingly reliant on data-driven insights to support decision making in areas such as transportation, public safety and environmental impact. However, city-level data often exists in heterogeneous fo…
Decision MakingGraph LearningPR-CapsNet: Pseudo-Riemannian Capsule Network with Adaptive Curvature Routing for Graph Learning
Capsule Networks (CapsNets) show exceptional graph representation capacity via dynamic routing and vectorized hierarchical representations, but they model the complex geometries of real\-world graphs poorly by fixed\-cur…
Graph Representation LearningGraph ClassificationGraph LearningOn Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series
Estimation of the conditional independence graph (CIG) of high-dimensional multivariate Gaussian time series from multi-attribute data is considered. Existing methods for graph estimation for such data are based on singl…
Graph LearningLearning Conditional Independence Differential Graphs From Time-Dependent Data
Estimation of differences in conditional independence graphs (CIGs) of two time series Gaussian graphical models (TSGGMs) is investigated where the two TSGGMs are known to have similar structure. The TSGGM structure is e…
Graph LearningMitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
Incomplete node features are ubiquitous in real-world scenarios such as user profiling and cold-start recommendation, which severely hinders the practical deployment of graph learning systems (e.g., GNNs). Existing solut…
Graph LearningGCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search
Graph Neural Networks (GNNs) have emerged as the state-of-the-art graph learning method. However, achieving efficient GNN inference on edge devices poses significant challenges, limiting their application in real-world e…
Graph LearningTowards agent-based-model informed neural networks
In this article, we present a framework for designing neural networks that remain consistent with the underlying principles of agent-based models. We begin by highlighting the limitations of standard neural differential …
Graph LearningLearning Invariant Graph Representations Through Redundant Information
Learning invariant graph representations for out-of-distribution (OOD) generalization remains challenging because the learned representations often retain spurious components. To address this challenge, this work introdu…
Representation LearningGraph LearningGraphBench: Next-generation graph learning benchmarking
Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often relying on narrow, task-specific datasets…
Molecular Property PredictionGraph LearningExplainable Graph Representation Learning via Graph Pattern Analysis
Explainable artificial intelligence (XAI) is an important area in the AI community, and interpretability is crucial for building robust and trustworthy AI models. While previous work has explored model-level and instance…
Graph Representation LearningGraph LearningEdged Weisfeiler-Lehman Algorithm
As a classical approach on graph learning, the propagation-aggregation methodology is widely exploited by many of Graph Neural Networks (GNNs), wherein the representation of a node is updated by aggregating representatio…
Graph ClassificationGraph LearningVS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing
Graph classification is a fundamental task in domains ranging from molecular property prediction to materials design. While graph neural networks (GNNs) achieve strong performance by learning expressive representations v…
Molecular Property PredictionGraph ClassificationGraph Learning